Mobile device with predictive routing engine

ABSTRACT

Some embodiments of the invention provide a mobile device with a novel route prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for the device&#39;s user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user&#39;s calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user. The device&#39;s prediction engine only relies on user-specific data stored on the device in some embodiments, relies only on user-specific data stored outside of the device by external devices/servers in other embodiments, and relies on user-specific data stored both by the device and by other devices/servers in other embodiments.

CLAIM OF BENEFIT TO PRIOR APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication 61/800,908, filed Mar. 15, 2013.

BACKGROUND

Mobile devices are moving towards having access to larger and largeramounts and varying types of personalized information, either stored onthe device itself or accessible to the device over a network (i.e., inthe cloud). This enables the owner/user of such a device to store andsubsequently easily access this information about their lives. Theinformation of use to the user of a mobile device may include theirpersonal calendar (i.e., stored in a calendar application), theire-mail, mapping information (e.g., locations of user-entered locations,user-requested routes, etc.).

However, at the moment, these devices require users to specificallyrequest information in order for the device to present the information.For instance, if a user wants a route to a particular destination, theuser must enter information into the mobile device (e.g., via atouchscreen, voice input, etc.) requesting the route. Given the amountof data accessible to the mobile devices, a device that leverages thisdata in order to predict the information needed by a user would beuseful.

BRIEF SUMMARY

Some embodiments of the invention provide a mobile device with a novelroute prediction engine that (1) can formulate predictions about currentor future destinations and/or routes to such destinations for thedevice's user, and (2) can relay information to the user about thesepredictions. In some embodiments, this engine includes amachine-learning engine that facilitates the formulation of predictedfuture destinations and/or future routes to destinations based onstored, user-specific data.

The user-specific data is different in different embodiments. In someembodiments, the stored, user-specific data includes data about anycombination of the following: (1) previous destinations traveled to bythe user, (2) previous routes taken by the user, (3) locations ofcalendared events in the user's calendar, (4) locations of events forwhich the user has electronic tickets, and (5) addresses parsed fromrecent e-mails and/or messages sent to the user. The device's predictionengine only relies on user-specific data stored on the device in someembodiments, relies only on user-specific data stored outside of thedevice by external devices/servers in other embodiments, and relies onuser-specific data stored both by the device and by otherdevices/servers in other embodiments.

The preceding Summary is intended to serve as a brief introduction tosome embodiments of the invention. It is not meant to be an introductionor overview of all inventive subject matter disclosed in this document.The Detailed Description that follows and the Drawings that are referredto in the Detailed Description will further describe the embodimentsdescribed in the Summary as well as other embodiments. Accordingly, tounderstand all the embodiments described by this document, a full reviewof the Summary, Detailed Description and the Drawings is needed.Moreover, the claimed subject matters are not to be limited by theillustrative details in the Summary, Detailed Description and theDrawings, but rather are to be defined by the appended claims, becausethe claimed subject matters can be embodied in other specific formswithout departing from the spirit of the subject matters.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features as described here are set forth in the appendedclaims. However, for purposes of explanation, several embodiments areset forth in the following figures.

FIG. 1 illustrates an example of a mobile device with a novel routeprediction engine.

FIG. 2 illustrates an example of a map application that displays in anon-intrusive manner predicted routes identified by the route predictionengine.

FIG. 3 illustrates a dynamic update by showing the map applicationswitching from one predicted destination/route to another predicteddestination/route.

FIG. 4 illustrates an example of the map application handling multiplepredicted destinations and/or routes.

FIG. 5 illustrates an example of the map application dynamicallydefining and updating its “recents” suggestions based on predicteddestinations received from the route.

FIG. 6 illustrates a notification center display of some embodiments.

FIG. 7 illustrates an example of how the device's notification servicesin some embodiments uses the predicted route data to provide automatednotification to a user.

FIG. 8 illustrates a detailed block diagram of the route predictionarchitecture of the mobile device of some embodiments of the invention.

FIG. 9 illustrates an approach for utilizing externally gathered data tohelp generate predicted destination data.

FIG. 10 illustrates a device that is similar to the device of FIG. 8except that its address parser also examines the ticket book, reminder,and calendar databases of the device to identify and extract newaddresses to include in the destination data storage of the device.

FIG. 11 conceptually illustrates an example of an architecture of amobile computing device on which some embodiments of the invention areimplemented.

FIG. 12 conceptually illustrates a map service operating environmentaccording to some embodiments.

DETAILED DESCRIPTION

In the following detailed description of the invention, numerousdetails, examples, and embodiments of the invention are set forth anddescribed. However, it will be clear and apparent to one skilled in theart that the invention is not limited to the embodiments set forth andthat the invention may be practiced without some of the specific detailsand examples discussed.

FIG. 1 illustrates an example of a mobile device 100 with a novel routeprediction engine 105. In some embodiments, the mobile device is asmartphone or a tablet computer with location identification, mappingand routing services. In addition to the route prediction engine 105,the device 100 includes a location identification engine 110, a routehistory data storage (e.g., database) 115, a number of secondary datastorages (e.g., databases) 120, a system clock 125 and a number ofoutput engines 130.

The route prediction engine 105 periodically performs automatedprocesses that formulate predictions about current or futuredestinations of the device and/or formulate routes to such destinationsfor the device based on, e.g., the information stored in the databases115 and 120. Based on these formulations, this engine then directs othermodules of the device to relay relevant information to the user.

In different embodiments, the route prediction engine 105 performs itsautomated processes with different frequencies. For instance, toidentify possible patterns of travel, it runs these processes once a dayin some embodiments, several times a day in other embodiments, severaltimes an hour in yet other embodiments, and several times a minute instill other embodiments. In addition, some embodiments allow the user ofthe device to configure how often or how aggressively the routeprediction engine should perform its automated processes.

The system clock 125 specifies the time and date at any given moment,while the location identification engine 110 specifies the currentlocation of the device. Different embodiments use different locationidentification engines. In some embodiments, this engine includes aglobal positioning system (GPS) engine that uses GPS data to identifythe current location of the user. In some of these embodiments, thisengine augments the GPS data with other terrestrial tracking data, suchas triangulated cellular tower data, triangulated radio tower data, andcorrelations to known access points (e.g., cell-ID, Wi-Fi ID/networkID), in order to improve the accuracy of the identified location. Inaddition, some embodiments use one or more of the types of terrestrialtracking data without GPS data.

Based on the location of the device and the time and date information,the route prediction engine 105 determines when it should perform itsprocesses, and what destinations and routes to predict. To formulate thepredicted destinations and/or predicted routes, the prediction enginealso uses previous location data that it retrieves from the routehistory database 115 and other secondary databases 120.

In some embodiments, the route history data storage 115 stores previousdestinations that the device recorded for previous routes taken by thedevice. Also, this data storage in some embodiments stores location andmotion histories regarding the routes taken by the device, includingidentification of locations at which user travel ended. Alternatively,or conjunctively, this storage stores in some embodiments other routedata (e.g., routes that are each specified in terms of a set of traveldirections). The secondary data storages 120 store additional locationsthat the route prediction engine 105 can use to augment the set ofpossible destinations for the device. Examples of such additionallocations include addresses extracted from recent electronic messages(e.g., e-mails or text messages), locations of calendared events,locations of future events for which the device has stored electronictickets, etc.

In some embodiments, the route prediction engine includes amachine-learning engine that facilitates the formulation of predictedfuture destinations and/or future routes to destinations based on thedestination, location, motion histories that it retrieves from the routehistory data storage 115 and the secondary data storages 120. In someembodiments, this machine-learning engine is used by additional modulesof the route prediction engine, including a predicted destinationgenerator and a predicted route generator.

The predicted destination generator uses the machine-learning engine tocreate a more complete set of possible destinations for a particulartime interval on a particular day. Various machine-learning engines canbe used to perform such a task. In general, machine-learning engines canformulate a solution to a problem expressed in terms of a set of knowninput variables and a set of unknown output variables by takingpreviously solved problems (expressed in terms of known input and outputvariables) and extrapolating values for the unknown output variables ofthe current problem. In the case of generating a more complete set ofpossible destinations for a particular time interval on a particularday, the machine-learning engine is used to filter the routedestinations stored in the route history data storage 115 and augmentthis set based on locations specified in the secondary data storages120.

The predicted route generator then uses the machine-learning engine tocreate sets of associated destinations, with each set specified as apossible route for traveling. In some embodiments, each set ofassociated destinations includes start and end locations (which aretypically called route destinations or endpoints), a number of locationsin between the start and end locations, and a number of motion recordsspecifying rate of travel (e.g., between the locations). In someembodiments, the predicted route generator uses the machine-learningengine to stitch together previously unrelated destination, location,and motion records into contiguous sets that specify potential routes.Also, in some embodiments, the route generator provides each of the setsof records that it generates to an external routing engine outside ofthe device (e.g., to a mapping service communicatively coupled to thedevice through a wireless network), so that the external routing servicecan generate the route for each generated set of associated records. Onesuch approach will further described below by reference to FIG. 9. Inother embodiments, the prediction route generator itself generates theroute for each set of associated records.

Once the route prediction engine 105 has one or more specified predictedroutes, it supplies its set of predicted routes and/or metadatainformation about these routes to one or more output engines 130. Theoutput engines then relay one or more relevant pieces of information tothe user of the device based on the data supplied by the routing engine.Examples of such output include: (1) displaying potential routes on amap, (2) dynamically updating potential routes on the map, (3)dynamically sorting and updating suggestions of possible destinations tosearch or to which to provide a route, (4) dynamically providing updatesto potential routes in the notification center that a user may manuallyaccess, (5) providing automatic prompts regarding current or futureroutes, etc.

Several such output presentations will now be described by reference toFIGS. 2-7. FIG. 2 illustrates an example of a map application thatdisplays in a non-intrusive manner predicted routes identified by theroute prediction engine. In some embodiments, the mobile device executesthis application and displays its output on its display screen when theuser has the map application operating in the foreground. In addition,the device in some embodiments transmits the output of the mapapplication to a display screen in a vehicle through an interface thatfacilitates communication between the device and the vehicle.Accordingly, the display presentations illustrated in FIG. 2 can beeither provided on the display screen of the mobile device or a displayscreen within a vehicle in which the user of the device is traveling.

The interface of some embodiments with the vehicle enables the device toprovide a safe driving experience. In addition to the device performingcertain tasks and presenting certain content (e.g., predicteddestination and/or routes) automatically, without the need for userinput, some embodiments additionally allow user input through voicecontrols, touch screen controls, and/or physical controls mounted on thedashboard or steering wheel, among other possibilities. Also, certainapplications and/or tasks may be automatically invoked based on sensorsof the electronic device or information provided to the electronicdevice by other devices, such as systems of the vehicle (through thevehicle-device interface). For example, route prediction) or other)tasks may be automatically invoked when the vehicle is started, or basedon a current location as provided by a GPS or other location indicationsensor.

This figure illustrates the operation of the map application in twostages 205 and 210 that correspond to two different instances in timeduring a trip from the user's home to the home of the user's aunt.Specifically, the first stage 205 corresponds to the start of the tripwhen the user has left his home 220. At this stage along the trip, themobile device's prediction engine has not yet predicted the destinationor the route to the destination. Accordingly, the map applicationprovides a display presentation 215 that simply shows the location ofthe device along the road being traveled by the vehicle. Thispresentation also provides on its left side the identity of the roadthat is being currently traversed, which in this example is I-280 North.In this embodiment, this presentation 215 is simple and does not have asupplementary audio component because this presentation is not meant todistract the user as the user has not affirmatively requested a route tobe identified or navigated.

As the vehicle continues along its path, the device's route predictionengine at some point identifies a predicted destination for the journeyand a route to this destination. In the example illustrated in FIG. 2,this destination is the home of the user's aunt 240. As the user onlyrarely travels from his home to his aunt's house, the prediction enginedid not immediately identify this destination, but instead needed togather additional data about the direction of the user's travel to helpidentify the possible destination for this journey.

In some embodiments, the route prediction engine of some embodimentsbegins attempting to predict a destination for the device oncedetermining that the device is in transit and therefore might want adestination. Different embodiments may use different factors orcombinations of factors to make such a determination. For instance, theroute prediction engine may use location information to identify thatthe device is now located on a road (e.g., I-280 North) and/or that thedevice is traveling at a speed associated with motorized vehicle travel.

The second stage 210 shows that once the device's route predictionengine identifies the aunt's house 240 as a possible destination andthen identifies a route from the device to this possible destination,the map application changes the display presentation 215 to the displaypresentation 225. Like the presentation 215, the presentation 225 hastwo parts. The first part 230 displays a route between the device'scurrent location 235 and the aunt's house 240. In some embodiments, thispart also displays a UI affordance (e.g., a selectable item) 245 forinitiating a vehicle navigation presentation so that the map applicationcan provide turn-by-turn navigation instructions.

The second part 250 of the display presentation 225 provides theidentity of the destination, some other data regarding this destination(e.g., the frequency of travel) and an ETA for the trip. Like the firstdisplay presentation 215, the second display presentation is rathernon-intrusive because this presentation is not meant to distract theuser as the user has not affirmatively requested a route to beidentified or navigated.

The example illustrated in FIG. 2 illustrates that in some embodiments,the device's map application can dynamically update its predicteddestination and route because the predicted route engine dynamicallyupdates its predictions as it gathers more data from the user'sdirection of travel. FIG. 3 presents a more concrete example of thisdynamic update as it shows the map application switching from onepredicted destination/route to another predicted destination/route. Thisfigure illustrates this example in three stages 305-315.

The first stage 305 shows the map application as providing a firstpresentation 322 of a first predicted route 320 to the house of theuser's mother 326 and some information 325 about the predicteddestination and the expected ETA. The second stage illustrates a secondpresentation 324 that is similar to the first presentation except thatthe user is shown to have reached an intersection 330 along thepredicted route. As shown in the left part of the second presentation aswell as the predicted route in the right part of the secondpresentation, the map application during the second stage is stillpredicting that the home of the user's mom is the eventual destinationor the trip.

The third stage 315 shows that instead of turning right along theintersection to continue on the route to the mom's house, the user hastaken a left turn towards the home of the user's father 328. Upon thisturn, the map application provides a third presentation 329 thatdisplays a second predicted route 335 to the father's house along withinformation 350 about the predicted destination and the expected ETA.

In many cases, the device's route prediction engine might concurrentlyidentify multiple possible destination and routes to these destinations.In these situations, the prediction engine ranks each predicteddestination or route based on a factor that quantifies the likelihoodthat they are the actual destination or route. This ranking can then beused to determine which destination or route is processed by the outputengine that receives the predictions.

In both of the above examples, the predicted destinations aredestination particular to the user (i.e., for other devices belonging toother people, the destination address predicted in stages 305 and 310would not be “Mom's House”). In some embodiments, the device's routeprediction engine uses stored contact information (e.g., an address bookentry for “Mom” listing a physical address) combined with route historyand motion history indicating that the particular physical address is afrequent destination in order to identify that the user is likelyheading to “Mom's House”.

FIG. 4 illustrates an example of the map application handling multiplepredicted destinations and/or routes. This example is illustrated interms of four operational stages 405-420 of the map application. Thefirst stage 405 is similar to the first stage 205 of FIG. 2. A user hasleft his house 406 and is moving north on I-280. In this stage, theroute prediction engine has not identified a possible destination or aroute to a possible destination.

In the second stage 410, the user approaches an exit that is close to acoffee shop 430 that the user frequently attends. Accordingly, as theuser reaches this exit, the prediction engine identifies the coffee shop430 as a likely destination and predicts the route 435 to the coffeeshop (or sends the predicted destination to a route generation server inorder to for the server to generate and return the route 435). Once themap application receives these predictions, the application provides thesecond presentation 440 that shows the coffee shop 430 and the predictedroute 435 to the coffee shop.

As the user reaches the exit 422, the route prediction engine identifiestwo other possible destinations for the user. Accordingly, the thirdstage 415 shows that as the user moves closer to the exit, three smallcircles 445 are added to the bottom of the presentation 450 of the mapapplication. These three circles connote the existence of threepredicted destinations/routes. The third stage 415 also shows the userperforming a swipe operation on the presentation to navigate to anotherof the predicted destinations/routes. The user can perform such anaction because in some embodiments the display (e.g., of the device orof the vehicle), which is displaying the presentation, has a touchsensitive screen. In addition to swipe gestures, some embodiments mayaccept other gestures, or selection of various affordances (e.g., leftand right or up and down navigation arrows) in order to cycle throughthe different options.

If the presentation is being shown on a non-touch sensitive screen of avehicle, the user can navigate to the next predicted destination/routethrough one of the keys, knobs, or other controls of the vehicle. Whilethe previous FIGS. 2 and 3 do not show user interaction, one of ordinaryskill in the art will recognize that the presentations displayed by themap application in these figures could be shown on a touch sensitivedisplay of the device, of the vehicle, etc.

Regardless of how the user navigates to the next predicteddestination/route, the map application presents the next predicteddestination/route upon receiving the user's input. The fourth stage 420of FIG. 4 illustrates the map application's presentation 455, whichshows a gym 460 and a route 465 to the gym as another predicteddestination/route. The map application did not initially show the routeto the gym in the third stage because the route prediction engineassigned a lower probability to the gym being the actual destination ascompared to the coffee shop, as the coffee shop has been a frequent stopof the user (as indicated in the second and third stages 410 and 415)while the gym has been a less frequent stop of the user (as indicated inthe fourth stage 420). In addition to using frequency of arrival atparticular destinations, the route prediction generator may use otherinformation, such as the frequency with which the user requests a routeto the destination (irrespective of whether the device actually travelsto the destination), frequency of arrival at the destination at theparticular day and time of day (e.g., the user may travel to a thirddestination in the area somewhat frequently, but only on the weekend oronly in the afternoon). As examples, the user's workplace and/or acoffee shop may be common morning destinations (especially weekdaymornings), whereas home or a favorite restaurant may be common nighttimedestinations when the device's current location is at the user's work.

In some embodiments, the map application uses the predicteddestination/route to dynamically define and update other parts of itsfunctionality in addition to or instead of its route guidance. FIG. 5illustrates an example of the map application dynamically defining andupdating its “recents” suggestions based on predicted destinationsreceived from the route prediction engine. This example is illustratedin terms of three operations stages 505, 510 and 515 of the device thatcorrespond to three different positions 520, 525 and 530 of the useralong a route.

In each stage, the map application display a “recents” window 535 thatopens when the search field 540 is selected. This window is meant toprovide suggestions for possible destinations to a user. When the mapapplication does not have a predicted destination, the recents windowdisplays initially pre-specified destinations, such as the user's homeand the user's work, as shown in the first stage 505. This stagecorresponds to a start of a trip 520. At this time, the predictionengine has not identified a predicted destination. In addition todisplaying the pre-specified destinations, some embodiments mayadditionally display for selection recently entered locations obtainedfrom recent tasks performed on the device or on another device by thesame user. For instance, the recents locations may include a location ofa restaurant for which the user recently searched in a web browser, theaddress of a contact that the user recently contacted (e.g., via e-mail,message, phone call, etc.), the location of a device of a contact thatthe user recently contacted (if the user has permission to acquire thatinformation), a source location of a recent route to the device'scurrent location, etc.

The second stage 510 shows that at a later position 525 along the trip,the route prediction engine identifies two possible destinations, whichare the Hamburger Palace and the Pot Sticker Delight. The predictionengine at this stage provides these two possible destinations to the mapapplication, having assigned the Hamburger Palace a higher probabilityof being the actual destination. Accordingly, in the second stage 510,the map application replaces in the recents window the default Home andWork destinations with the Hamburger Palace and Pot Sticker Delightdestinations as these have been assigned higher probabilities of beingthe eventual destination than the default choices (which may also havebeen assigned some small but non-zero probability of being the user'sdestination). Based on the assignment of a higher probability toHamburger Palace as the eventual destination, the map applicationdisplays the Hamburger Palace higher than the Pot Sticker Delight onthis page.

However, after the user passes an intersection 550 shown in the thirdposition 530 along the route, the prediction engine (which regularlyrecalculates probabilities for possible destinations, in someembodiments) determines that the Pot Sticker Delight restaurant now hasa higher probability than Hamburger Palace of being the eventualdestination. The engine notifies the map application of this change, andin response, the map application swaps the order of these two choices inthe recents window 560. In some embodiments, the prediction engine sendsa list of possible destinations and their probabilities to the mapapplication (e.g., a particular number of destinations, or alldestinations above a particular probability) on a regular basis. Inother embodiments, the map application sends a request to the predictionengine for a given number of possible destinations with the highestprobabilities in a particular order.

In addition to the map application shown in the preceding four figures,many other applications operating on the mobile device can be clientsfor the predictions made by this device's route prediction engine. Forinstance, as illustrated by FIGS. 6 and 7, notification services of thedevice can be such clients. FIG. 6 illustrates a notification centerdisplay 605 of some embodiments. This notification center display is awindow that can be manually requested by a user of the device wheneverthe user wishes to see an overview of alerts that are provided byvarious applications being executed on the device (e.g., voicemails andmissed calls from the phone application, text messages from a messagingapplication, etc.).

The notification center display 605 of some embodiments includes atraffic tab 610 that, when selected, illustrates information about thetraffic along predicted and/or in progress routes for the user. FIG. 6illustrates the type of information that the traffic tab can provide interms of the traffic along a predicted route on two consecutive days.Each day is shown in terms of three operational stages of the device.

On the first day, the user manually pulls on the notification centeraffordance 650 in the first stage 615. As shown by the second stage 617,this operation results in the presentation of the notification centerdisplay 605 a. This display presents the enabled notification feature ofthis display (as indicated by the greyed out color of the notificationaffordance 625) and also presents a variety of notification alerts froma variety of applications. The second stage 617 also shows the user'stouch selection of the traffic affordance 610.

As shown by the third stage 619, the selection of this affordanceresults in the presentation 605 b of the traffic window, which statesthat traffic along I-280 north is typical for the predicted time of theuser's departure. The expression of traffic as typical or atypical ishighly useful because certain routes are always congested. Accordingly,a statement that the route is congested might not help the user. Rather,knowing that the traffic is better than usual, worse than usual, or thesame as usual is more useful for the user.

In some embodiments, the notification services provide such normativeexpressions of traffic because the route prediction engine (1) predictslikely routes that the user might take at different time periods basedon the user's historical travel patterns, and (2) compares the trafficalong these routes to historical traffic levels along these routes. Thisengine then provides not only one or more predicted routes for the userbut also normative quantification of the level of traffic along each ofthe predicted routes. When the engine provides more than one predictedroute, the engine also provides probabilities for each predicted routethat quantifies the likelihood that the predicted route is the actualroute. Based on these probabilities, the notification manager candisplay traffic information about the most likely route, or creates astacked, sorted display of such traffic information, much like thesorted, stacked display of routes explained above by reference to FIG.4. That is, the user could perform a swipe interaction (or performanother interaction with the device) in order to cause the notificationcenter to provide information for a different possible route (e.g.,traffic information for US-101). In addition to providing probabilitiesfor different routes to different destinations, the route predictionengine may identify two (or more) probabilities for two different routesto the same destination (e.g., if the user often takes two differentroutes to and/or from work based on his own assessment of the traffic).

On the second day, the user again manually pulls on the notificationcenter affordance 650 in the first stage 621. As shown by the secondstage 623, this operation again results in the presentation of thenotification center display 605 c. This display presents the enablednotification feature of this display (as indicated by the greyed outcolor of the notification affordance 625) and also presents a variety ofnotification alerts from a variety of applications. The second stage 623also shows the user's touch selection of the traffic affordance 610.

As shown by the third stage 627, the selection of this affordance againresults in a presentation 605 d of the traffic window. In this case, thewindow states that traffic along I-280 north is worse than usual for thepredicted time of the user's departure and that the user should considerleaving a little earlier than usual. The notification services providesthis notice because the route prediction engine (1) has predicted thatthe user will likely take I-280 north, and (2) has compared today'straffic with historical traffic levels along this route to determinethat traffic today is worse than usual.

While specific names are given to the two tabs of the notificationcenter (“Notifications” and “Traffic”), one of ordinary skill in the artwill recognize that different names or icons may be used to representthese tabs. For instance, the “Traffic” tab is called the “Today” tab insome embodiments. Similarly, other specific UI names and icons may berepresented differently in different embodiments.

FIG. 7 provides another example of how the device's notificationservices in some embodiments uses the predicted route data to provideautomated notification to a user. In this example, the device has anotification service called Traffic Alerts, which when enabled, allowsthe user to receive traffic alerts as automated prompts while the devicescreen is on or off.

The example in this figure is described in terms of four stages ofoperations of the device. The first stage 705 shows the user selectingthe notification manager icon on a page 707 of the device 700. In someembodiments, the notification manager does not have an icon on the page707, but rather is made available through a setting menu that ispresented when a setting icon on page 707 or another page of thedevice's UI.

The second stage 710 shows the presentation of several notificationcontrols, one of which is the traffic alert affordance 712. Thisaffordance allows the traffic alert service of the notification managerto be turned on or off. The second stage shows the user turning on thetraffic alert service, while the third stage 715 shows the notificationpage after this service has been turned on. The third stage 715 alsoshows the user turning off the screen of the device by pressing on ascreen-off button 732 of the device.

The fourth stage 720 is a duration of time after the user has turned offthe screen of the device. During this duration, the device's routeprediction engine has identified that the user will likely take apredicted route and has also determined that this route is congested somuch that the user will not likely make a 10 am meeting at a particularlocation that is indicated in the user's calendar. In this case, theprediction engine may have generated the predicted route based on theinformation in the user's calendar as well as the time of day andhistorical travel data for that time of day.

The prediction engine relays this information to the notificationmanager of the device, and in response, the notification mangergenerates a traffic alert prompt 745 that is illustrated in the fourthstage. This prompt notifies the user that traffic along the user'spredicted route is worse than usual and that the user might wish toconsider leaving 20 minutes earlier so that the user can make his 10 ammeeting. By utilizing the calendar information, the device is able toidentify the traffic congestion and alert the user while the user stillhas time to leave early for the meeting. When the user is on route andthe device determines that the user will not be able to arrive at themeeting on time, the device can notify the user that he will be late,and enable the user to notify others participating in the meeting.

Instead of such a traffic alert, the notification manager in otherembodiments provides other types of alerts, such as the normative ones(e.g., worse than usual, better than usual) described above. Also, whilethe example illustrated in FIG. 7 shows the traffic alert while thescreen is off, the notification manager in some embodiments alsoprovides such notifications while the screen is on, or only while thescreen is on.

FIG. 8 illustrates a more detailed block diagram of the route predictionarchitecture of the mobile device 800 of some embodiments of theinvention. In some embodiments, the mobile device is a smartphone or atablet computer with location identification, mapping and routingservices. FIG. 8 illustrates the mobile device's map manager 810 thatperforms the map output functions described above by reference to FIGS.2-5, based on the destination/route data predicted by the device's routeprediction engine 105. It also illustrates the device's notificationmanager 815 that perform the notification functions described above byreference to FIGS. 6 and 7, based on the predicted data.

In addition to the route prediction engine 105, the map manager 810 andthe notification manager 815, the device 800 also includes a locationidentification engine 110, a route history data storage (e.g., database)825, a location data storage 830, motion data storage 835, a datastorage filter 840, an address parser 845, email and message processors850 and 855, an external map service manager 860 and a network interface865. This device includes a number of other modules (e.g., a systemclock) that are not shown here in order to not obscure the descriptionof this device with unnecessary detail.

The data storages 825, 830 and 835 store user-specific travel data thatthe route prediction engine 105 uses (1) to formulate predictions aboutcurrent or future destinations and/or routes to such destinations forthe device's user, and (2) to provide this information to thenotification manager 815 and the map manager 810 in order to relayrelevant information to the user. The user-specific data is different indifferent embodiments. In some embodiments, the destination data storage825 stores data about previous destinations traveled to by the user, andaddresses parsed from recent e-mails and/or messages sent to the user.The address parser 845 (1) examines new emails and messages receivedfrom the email and message processors 850 and 855 to identify and parseaddresses in these emails and messages, (2) for each extracted address,determines whether the address is stored in the destination storage 825already, and (3) stores each new address (that was not previously storedin the storage 825) in the destination storage 825. In some embodiments,the address parser 845 uses known techniques to identify and parseaddresses in these messages (i.e., the techniques that enable devices tomake addresses selectable within an e-mail or message).

The location data storage 830 in some embodiments stores locations alongroutes that the device previously traveled (e.g., GPS coordinates of thedevice at intervals of time along a route). The motion data storage 835in some embodiments stores travel speeds of the device along previouslytraveled routes. In some embodiments, this includes the travel speedsfor one or more locations stored in the location data storage 830 (e.g.,the speed at each of the GPS coordinates, or speed traveled between twocoordinates). The filter 840 periodically examines the destination,location and motion data stored in the storages 825, 830 and 835, andremoves any data that is “stale.” In some embodiments, the filter'scriterion for staleness is expressed in terms of the age of the data andthe frequency of its use in predicting new destinations and/or newroutes. Also, in some embodiments, the filter uses different criteriafor measuring the staleness of different types of data. For instance,some embodiments filter parsed address data (provided by the addressparser 845) that is older than a certain number of days (e.g., one day),while filtering destination, location and motion data related toprevious routes of the device based on the age of the data and itsfrequency of use.

Together, the route prediction engine uses the destination, location andmotion data stored in the data storages 825, 830 and 835, along withadditional inputs such as the system clock and location identificationengine 110, to identify predicted destinations and routes between thesedestinations. The route prediction engine 105 periodically performsautomated processes that formulate predictions about current or futuredestinations of the device and/or formulate routes to such destinationsfor the device. Based on these formulations, this engine then directsthe notification manager 815 and the map manager 810 to relay relevantinformation to the user.

The prediction engine 105 includes a prediction processor 807, apredicted destination generator 870, a machine learning engine 875 and apredicted route generator 880. The prediction processor 807 isresponsible for initiating the periodic automated processing of theengine, and serves as the central unit for coordinating much of theoperations of the prediction engine.

In different embodiments, the prediction processor 807 initiates theautomated processes with different frequencies. For instance, toidentify possible patterns of travel, it runs these processes once a dayin some embodiment, several times a day in other embodiments, severaltimes an hour in yet other embodiments, and several times a minute instill other embodiments. In some embodiments, the prediction processor807 initiates the automated process on a particular schedule (e.g., onceper day) and additionally initiates the process when something changes(e.g., when a user adds an address to a calendar event or when somethingis added to a history). In some embodiments, the prediction processor807 runs less frequently when the device is running on battery powerthan when it is plugged in to an external power source. In addition,some embodiments perform the automated prediction processes morefrequently upon detection that the device is traveling along a road orat a speed above a particular threshold generally associated with motorvehicles (e.g., 20 mph, 30 mph, etc.). Furthermore, some embodimentsallow the user of the device to configure how often or how aggressivelythe route prediction engine should perform its automated processes.

Whenever the prediction processor 807 initiates a route predictionoperation, it directs the predicted destination generator 870 togenerate a complete list of possible destinations based on previousroute destinations and parsed new address locations stored in thedestination data storage 825. In some embodiments, the predictiondestination generator uses the machine-learning engine 875 to create amore complete set of possible destinations for a particular location ofthe device (as specified by the location identification engine 110) fora particular time interval on a particular day. Various machine-learningengines can be used to perform such a task. In general, machine-learningengines can formulate a solution to a problem expressed in terms of aset of known input variables and a set of unknown output variables bytaking previously solved problems (expressed in terms of known input andoutput variables) and extrapolating values for the unknown outputvariables of the current problem. In the case of generating a morecomplete set of possible destinations for a particular time interval ona particular day, the machine-learning engine is used to filter thestored route destinations and augment this set based on addresses parsedby the parser 845.

In some embodiments, the machine-learning engine 875 not onlyfacilitates the formulation of predicted future destinations, but italso facilitates the formulation of predicted routes to destinationsbased on the destination, location, and motion histories that are storedin the data storages 825, 830 and 835. For instance, in someembodiments, the prediction route generator 880 uses themachine-learning engine to create sets of associated destinations, witheach set specified as a possible route for traveling. In someembodiments, each set of associated destinations includes start and endlocations (which are typically called route destinations or endpoints),a number of locations in between the start and end locations, and anumber of motion records specifying rate of travel (e.g., between thelocations). In some embodiments, the predicted route generator uses themachine-learning engine to stitch together previously unrelateddestination, location, and motion records into contiguous sets thatspecify potential routes. In some embodiments, the destination historyincludes both addresses that are received as explicit addresses (e.g.,addresses input by the user or received in an e-mail) as well asadditional destinations derived from previous locations in the locationhistory of the device (e.g., the location data storage 830).

In some embodiments, the location history is used to predict bothdestinations and routes. By identifying locations at which the devicestopped traveling (or at least stopped traveling at motor vehiclespeeds), and subsequently stayed within locations associated with asingle address for at least a threshold amount of time, the predicteddestination generator can extrapolate possible destinations from thelocation data 830. However, this use of the motion history mayoccasionally lead to false positives for destinations (e.g., when theuser is stuck in a major traffic jam). Accordingly, some embodimentsalso identify (through an interface with a vehicle to which the deviceconnects) that the vehicle has been turned off and/or that subsequentmotion is at low speed (in terms of change of location coordinates) andfollows a movement (e.g., based on accelerometer data) associated withleaving the interior of a vehicle (e.g., small-scale movement associatedwith walking) Also, in some embodiments, the route generator through theprediction processor 807 provides each of the sets of records that itgenerates to an external routing engine outside of the device so thatthe external routing service can generate the route for each generatedset of associated records. To communicate with the external routingengine, the prediction processor 807 uses the external map servicemanager 860, which through the network interface 865 and a network(e.g., a wireless network) communicatively couples to the device and theexternal map service. One such approach will further described below byreference to FIG. 9. In other embodiments, the prediction routegenerator itself generates the route for each set of associated records.

Once the route generator 880 has one or more specified predicted routes,the prediction processor 807 supplies the generated set of predictedroutes and/or metadata information about these routes to thenotification manager 815 and the map manager 810 for relay of one ormore relevant pieces of route information to the user of the device. Forinstance, based on the predicted destination/route data supplied by thedevice's route prediction engine 105, the map manager 810 performs themap output functions described above by reference to FIGS. 2-5, whilethe notification manager 815 that perform the notification functionsdescribed above by reference to FIGS. 6 and 7, in some embodiments ofthe invention. In addition, in some embodiments, the map manager 810 mayprovide information to the notification manager (e.g., trafficinformation) or vice versa.

In the example illustrated in FIG. 8, the device's route predictionengine 105 only relies on user-specific data stored on the device insome embodiments. However, in other embodiments, this engine can alsobased its operations on user-specific data that is identified outside ofthe device by external devices/servers.

FIG. 9 illustrates one such approach for utilizing externally gathereddata to help generate predicted destination data. This figure alsoprovides an example of an external route generation service that thedevice 800 can use to generate its predicted routes. This figureillustrates a system 900 that includes the device 800, a set of servers905 and a desktop computer 910. While shown as a desktop, one ofordinary skill in the art will recognize that this may be a laptop orother computer as well. The server set 905 is a collection of one ormore servers that provide a cloud synchronization service forsynchronizing certain services (e.g., email, contact, calendar,document, etc.) across all the devices/computers of a user. Numeroususers can subscribe to this service to synchronize the content on eachof their own devices/computers.

The computer 910 is associated with the device 800, e.g., belongs to thesame cloud synchronization account as the device 800. This computerincludes an address parser 920 with similar functionality to the addressparser 845 of FIG. 8. This parser identifies and extracts new addresslocations in recent e-mails and/or messages sent to the user, and storesthe extracted locations in the destination database 925.

The computer 910 also has client service manager 930 and networkinterface 935 that push any newly stored locations to an address datastorage 940 in the server set 905 for the account associated with thedevice 800 and the computer 910. In some embodiments, the server set 905communicatively couples to the device 800 and the computer 910 throughthe Internet 970. Accordingly, in these embodiments, the servers haveone or more web servers 937 that facilitate communications between theback-end servers and the device or the computer.

The servers 905 include a cloud service manager 965 that coordinates allcommunication that it receives from the device 800 and the computer 910.It also has an address data storage 940 that stores addresses parsed bythe parsers 845 and 920 as well as locations searched in the device andthe computer. In some embodiments, the storage 940 also stores previousdestinations and/or previous locations traveled by the user of thedevice. In other embodiments, however, information about the user'sprevious destinations and/or locations are not stored in the addressdata storage in order to respect and maintain the privacy of the user.To the extent that addresses are stored in the storage 940 in someembodiments, the storage 940 stores the addresses in an encrypted mannerso that only keys residing on the device or the desktop can decrypt andgain access to such address data.

After storing any newly parsed address that it receives in the datastorage 940, the cloud service manager 965 directs a destinationpublisher 955 to publish this address to any device or computerassociated with the same synchronization account as the computer 910.Accordingly, this newly parsed data will get pushed by the publisher tothe destination storage 825 through the web server 937, the Internet,the network interface 865 and the external map service manager 860. Oncethe data is in this storage 825, the route prediction engine 105 can useit to formulate its predicted destinations.

In some embodiments, the servers have one or more filters for filteringout stale data in the address data storage 940. The servers also includea route generator 960, which is the engine used by the device's routeprediction engine 105 to generate one or more routes for each set ofdestination, location and/or moving record that the engine 105 providesto it for route generation. In some embodiments, each module of theserver set 905 that is shown as a single block might be implemented byone or more computers dedicated to the particular task of the module.Alternatively, or conjunctively, two or more modules of the server setmight execute on the same computer. Furthermore, the differentfunctionalities performed by the set of servers might be performed indisparate geographic locations (e.g., a set of servers at one locationfor the route generation with the address database stored at a differentlocation). In some embodiments, the route generation function might beperformed by servers belonging to a first organization, while a secondorganization stores the addresses in its cloud storage, withcommunication between the two organization either performed directly orthrough the device 800 as an intermediary.

In addition to parsing emails and messages to identify possibledestination locations, the device of some embodiments examines othersecondary sources to identify other potential destinations. Examples ofsuch other candidate destinations include locations of calendared eventsin the user's calendar, locations of events for which the user haselectronic tickets, and locations associated with reminders. FIG. 10illustrates a device 1000 that is similar to the device 800 except thatits address parser 845 also examines the ticket book, reminder, andcalendar databases of the device to identify and extract new addressesto include in the destination data storage of the device. In someembodiments, such parsing of calendar, reminder and ticket data isperformed on each device or computer of the user that is associated witha cloud synchronization account and any newly identified address ispushed to each device or computer associated with the account.

Many of the above-described features and applications are implemented assoftware processes that are specified as a set of instructions recordedon a computer readable storage medium (also referred to as computerreadable medium). When these instructions are executed by one or morecomputational or processing unit(s) (e.g., one or more processors, coresof processors, or other processing units), they cause the processingunit(s) to perform the actions indicated in the instructions. Examplesof computer readable media include, but are not limited to, CD-ROMs,flash drives, random access memory (RAM) chips, hard drives, erasableprogrammable read-only memories (EPROMs), electrically erasableprogrammable read-only memories (EEPROMs), etc. The computer readablemedia does not include carrier waves and electronic signals passingwirelessly or over wired connections.

In this specification, the term “software” is meant to include firmwareresiding in read-only memory or applications stored in magnetic storagewhich can be read into memory for processing by a processor. Also, insome embodiments, multiple software inventions can be implemented assub-parts of a larger program while remaining distinct softwareinventions. In some embodiments, multiple software inventions can alsobe implemented as separate programs. Finally, any combination ofseparate programs that together implement a software invention describedhere is within the scope of the invention. In some embodiments, thesoftware programs, when installed to operate on one or more electronicsystems, define one or more specific machine implementations thatexecute and perform the operations of the software programs.

The mapping and navigation applications of some embodiments operate onmobile devices, such as smart phones (e.g., iPhones®) and tablets (e.g.,iPads®). FIG. 11 is an example of an architecture 1100 of such a mobilecomputing device. Examples of mobile computing devices includesmartphones, tablets, laptops, etc. As shown, the mobile computingdevice 1100 includes one or more processing units 1105, a memoryinterface 1110 and a peripherals interface 1115.

The peripherals interface 1115 is coupled to various sensors andsubsystems, including a camera subsystem 1120, a wireless communicationsubsystem(s) 1125, an audio subsystem 1130, an I/O subsystem 1135, etc.The peripherals interface 1115 enables communication between theprocessing units 1105 and various peripherals. For example, anorientation sensor 1145 (e.g., a gyroscope) and an acceleration sensor1150 (e.g., an accelerometer) is coupled to the peripherals interface1115 to facilitate orientation and acceleration functions.

The camera subsystem 1120 is coupled to one or more optical sensors 1140(e.g., a charged coupled device (CCD) optical sensor, a complementarymetal-oxide-semiconductor (CMOS) optical sensor, etc.). The camerasubsystem 1120 coupled with the optical sensors 1140 facilitates camerafunctions, such as image and/or video data capturing. The wirelesscommunication subsystem 1125 serves to facilitate communicationfunctions. In some embodiments, the wireless communication subsystem1125 includes radio frequency receivers and transmitters, and opticalreceivers and transmitters (not shown in FIG. 11). These receivers andtransmitters of some embodiments are implemented to operate over one ormore communication networks such as a GSM network, a Wi-Fi network, aBluetooth network, etc. The audio subsystem 1130 is coupled to a speakerto output audio (e.g., to output voice navigation instructions).Additionally, the audio subsystem 1130 is coupled to a microphone tofacilitate voice-enabled functions, such as voice recognition (e.g., forsearching), digital recording, etc.

The I/O subsystem 1135 involves the transfer between input/outputperipheral devices, such as a display, a touch screen, etc., and thedata bus of the processing units 1105 through the peripherals interface1115. The I/O subsystem 1135 includes a touch-screen controller 1155 andother input controllers 1160 to facilitate the transfer betweeninput/output peripheral devices and the data bus of the processing units1105. As shown, the touch-screen controller 1155 is coupled to a touchscreen 1165. The touch-screen controller 1155 detects contact andmovement on the touch screen 1165 using any of multiple touchsensitivity technologies. The other input controllers 1160 are coupledto other input/control devices, such as one or more buttons. Someembodiments include a near-touch sensitive screen and a correspondingcontroller that can detect near-touch interactions instead of or inaddition to touch interactions.

The memory interface 1110 is coupled to memory 1170. In someembodiments, the memory 1170 includes volatile memory (e.g., high-speedrandom access memory), non-volatile memory (e.g., flash memory), acombination of volatile and non-volatile memory, and/or any other typeof memory. As illustrated in FIG. 11, the memory 1170 stores anoperating system (OS) 1172. The OS 1172 includes instructions forhandling basic system services and for performing hardware dependenttasks.

The memory 1170 also includes communication instructions 1174 tofacilitate communicating with one or more additional devices; graphicaluser interface instructions 1176 to facilitate graphic user interfaceprocessing; image processing instructions 1178 to facilitateimage-related processing and functions; input processing instructions1180 to facilitate input-related (e.g., touch input) processes andfunctions; audio processing instructions 1182 to facilitateaudio-related processes and functions; and camera instructions 1184 tofacilitate camera-related processes and functions. The instructionsdescribed above are merely exemplary and the memory 1170 includesadditional and/or other instructions in some embodiments. For instance,the memory for a smartphone may include phone instructions to facilitatephone-related processes and functions. Additionally, the memory mayinclude instructions for a mapping and navigation application as well asother applications. The above-identified instructions need not beimplemented as separate software programs or modules. Various functionsof the mobile computing device can be implemented in hardware and/or insoftware, including in one or more signal processing and/or applicationspecific integrated circuits.

While the components illustrated in FIG. 11 are shown as separatecomponents, one of ordinary skill in the art will recognize that two ormore components may be integrated into one or more integrated circuits.In addition, two or more components may be coupled together by one ormore communication buses or signal lines. Also, while many of thefunctions have been described as being performed by one component, oneof ordinary skill in the art will realize that the functions describedwith respect to FIG. 11 may be split into two or more integratedcircuits.

Some embodiments include electronic components, such as microprocessors,storage and memory that store computer program instructions in amachine-readable or computer-readable medium (alternatively referred toas computer-readable storage media, machine-readable media, ormachine-readable storage media). Some examples of such machine-readablemedia include RAM, ROM, read-only compact discs (CD-ROM), recordablecompact discs (CD-R), rewritable compact discs (CD-RW), read-onlydigital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a varietyof recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.),flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.),magnetic and/or solid state hard drives, read-only and recordableBlu-Ray® discs, ultra density optical discs, any other optical ormagnetic media, and floppy disks. The machine-readable media may store aprogram that is executable by at least one processing unit and includessets of instructions for performing various operations. Examples ofprograms or code include machine code, such as is produced by acompiler, and files including higher-level code that are executed by acomputer, an electronic component, or a microprocessor using aninterpreter.

While the above discussion primarily refers to microprocessor ormulti-core processors that execute software, some embodiments areperformed by one or more integrated circuits, such as applicationspecific integrated circuits (ASICs), customized ASICs or fieldprogrammable gate arrays (FPGAs). In some embodiments, such integratedcircuits execute instructions that are stored on the circuit itself. Inaddition, some embodiments execute software stored in programmable logicdevices (PLDs), ROM, or RAM devices.

As used in this specification and any claims of this application, theterms “computer”, “server”, “processor”, and “memory” all refer toelectronic or other technological devices. These terms exclude people orgroups of people. For the purposes of the specification, the termsdisplay or displaying means displaying on an electronic device. As usedin this specification and any claims of this application, the terms“computer readable medium,” “computer readable media,” and “machinereadable medium” are entirely restricted to tangible, physical objectsthat store information in a form that is readable by a computer. Theseterms exclude any wireless signals, wired download signals, and anyother ephemeral signals.

As mentioned above, various embodiments may operate within a map serviceoperating environment. FIG. 12 illustrates a map service operatingenvironment, according to some embodiments. A map service 1230 (alsoreferred to as mapping service) may provide map services for one or moreclient devices 1202 a-1202 c in communication with the map service 1230through various communication methods and protocols. A map service 1230in some embodiments provides map information and other map-related data,such as two-dimensional map image data (e.g., aerial view of roadsutilizing satellite imagery), three-dimensional map image data (e.g.,traversable map with three-dimensional features, such as buildings),route and direction calculation (e.g., ferry route calculations ordirections between two points for a pedestrian), real-time navigationdata (e.g., turn-by-turn visual navigation data in two or threedimensions), location data (e.g., where is the client device currentlylocated), and other geographic data (e.g., wireless network coverage,weather, traffic information, or nearby points-of-interest). In variousembodiments, the map service data may include localized labels fordifferent countries or regions; localized labels may be utilized topresent map labels (e.g., street names, city names, points of interest)in different languages on client devices. Client devices 1202 a-1202 cmay utilize these map services by obtaining map service data. Clientdevices 1202 a-1202 c may implement various techniques to process mapservice data. Client devices 1202 a-1202 c may then provide map servicesto various entities, including, but not limited to, users, internalsoftware or hardware modules, and/or other systems or devices externalto the client devices 1202 a-1202 c.

In some embodiments, a map service is implemented by one or more nodesin a distributed computing system. Each node may be assigned one or moreservices or components of a map service. Some nodes may be assigned thesame map service or component of a map service. A load balancing node insome embodiments distributes access or requests to other nodes within amap service. In some embodiments a map service is implemented as asingle system, such as a single server. Different modules or hardwaredevices within a server may implement one or more of the variousservices provided by a map service.

A map service in some embodiments provides map services by generatingmap service data in various formats. In some embodiments, one format ofmap service data is map image data. Map image data provides image datato a client device so that the client device may process the image data(e.g., rendering and/or displaying the image data as a two-dimensionalor three-dimensional map). Map image data, whether in two or threedimensions, may specify one or more map tiles. A map tile may be aportion of a larger map image. Assembling together the map tiles of amap produces the original map. Tiles may be generated from map imagedata, routing or navigation data, or any other map service data. In someembodiments map tiles are raster-based map tiles, with tile sizesranging from any size both larger and smaller than a commonly-used 256pixel by 256 pixel tile. Raster-based map tiles may be encoded in anynumber of standard digital image representations including, but notlimited to, Bitmap (.bmp), Graphics Interchange Format (.gif), JointPhotographic Experts Group (.jpg, .jpeg, etc.), Portable NetworksGraphic (.png), or Tagged Image File Format (.tiff). In someembodiments, map tiles are vector-based map tiles, encoded using vectorgraphics, including, but not limited to, Scalable Vector Graphics (.svg)or a Drawing File (.drw). Some embodiments also include tiles with acombination of vector and raster data. Metadata or other informationpertaining to the map tile may also be included within or along with amap tile, providing further map service data to a client device. Invarious embodiments, a map tile is encoded for transport utilizingvarious standards and/or protocols, some of which are described inexamples below.

In various embodiments, map tiles may be constructed from image data ofdifferent resolutions depending on zoom level. For instance, for lowzoom level (e.g., world or globe view), the resolution of map or imagedata need not be as high relative to the resolution at a high zoom level(e.g., city or street level). For example, when in a globe view, theremay be no need to render street level artifacts as such objects would beso small as to be negligible in many cases.

A map service in some embodiments performs various techniques to analyzea map tile before encoding the tile for transport. This analysis mayoptimize map service performance for both client devices and a mapservice. In some embodiments map tiles are analyzed for complexity,according to vector-based graphic techniques, and constructed utilizingcomplex and non-complex layers. Map tiles may also be analyzed forcommon image data or patterns that may be rendered as image textures andconstructed by relying on image masks. In some embodiments, raster-basedimage data in a map tile contains certain mask values, which areassociated with one or more textures. Some embodiments also analyze maptiles for specified features that may be associated with certain mapstyles that contain style identifiers.

Other map services generate map service data relying upon various dataformats separate from a map tile in some embodiments. For instance, mapservices that provide location data may utilize data formats conformingto location service protocols, such as, but not limited to, RadioResource Location services Protocol (RRLP), TIA 801 for Code DivisionMultiple Access (CDMA), Radio Resource Control (RRC) position protocol,or LTE Positioning Protocol (LPP). Embodiments may also receive orrequest data from client devices identifying device capabilities orattributes (e.g., hardware specifications or operating system version)or communication capabilities (e.g., device communication bandwidth asdetermined by wireless signal strength or wire or wireless networktype).

A map service may obtain map service data from internal or externalsources. For example, satellite imagery used in map image data may beobtained from external services, or internal systems, storage devices,or nodes. Other examples may include, but are not limited to, GPSassistance servers, wireless network coverage databases, business orpersonal directories, weather data, government information (e.g.,construction updates or road name changes), or traffic reports. Someembodiments of a map service may update map service data (e.g., wirelessnetwork coverage) for analyzing future requests from client devices.

Various embodiments of a map service may respond to client devicerequests for map services. These requests may be a request for aspecific map or portion of a map. Some embodiments format requests for amap as requests for certain map tiles. In some embodiments, requestsalso supply the map service with starting locations (or currentlocations) and destination locations for a route calculation. A clientdevice may also request map service rendering information, such as maptextures or style sheets. In at least some embodiments, requests arealso one of a series of requests implementing turn-by-turn navigation.Requests for other geographic data may include, but are not limited to,current location, wireless network coverage, weather, trafficinformation, or nearby points-of-interest.

A map service, in some embodiments, analyzes client device requests tooptimize a device or map service operation. For instance, a map servicemay recognize that the location of a client device is in an area of poorcommunications (e.g., weak wireless signal) and send more map servicedata to supply a client device in the event of loss in communication orsend instructions to utilize different client hardware (e.g.,orientation sensors) or software (e.g., utilize wireless locationservices or Wi-Fi positioning instead of GPS-based services). In anotherexample, a map service may analyze a client device request forvector-based map image data and determine that raster-based map databetter optimizes the map image data according to the image's complexity.Embodiments of other map services may perform similar analysis on clientdevice requests and as such the above examples are not intended to belimiting.

Various embodiments of client devices (e.g., client devices 1202 a-1202c) are implemented on different portable-multifunction device types.Client devices 1202 a-1202 c utilize map service 1230 through variouscommunication methods and protocols. In some embodiments, client devices1202 a-1202 c obtain map service data from map service 1230. Clientdevices 1202 a-1202 c request or receive map service data. Clientdevices 1202 a-1202 c then process map service data (e.g., render and/ordisplay the data) and may send the data to another software or hardwaremodule on the device or to an external device or system.

A client device, according to some embodiments, implements techniques torender and/or display maps. These maps may be requested or received invarious formats, such as map tiles described above. A client device mayrender a map in two-dimensional or three-dimensional views. Someembodiments of a client device display a rendered map and allow a user,system, or device providing input to manipulate a virtual camera in themap, changing the map display according to the virtual camera'sposition, orientation, and field-of-view. Various forms and inputdevices are implemented to manipulate a virtual camera. In someembodiments, touch input, through certain single or combination gestures(e.g., touch-and-hold or a swipe) manipulate the virtual camera. Otherembodiments allow manipulation of the device's physical location tomanipulate a virtual camera. For instance, a client device may be tiltedup from its current position to manipulate the virtual camera to rotateup. In another example, a client device may be tilted forward from itscurrent position to move the virtual camera forward. Other input devicesto the client device may be implemented including, but not limited to,auditory input (e.g., spoken words), a physical keyboard, mouse, and/ora joystick.

Some embodiments provide various visual feedback to virtual cameramanipulations, such as displaying an animation of possible virtualcamera manipulations when transitioning from two-dimensional map viewsto three-dimensional map views. Some embodiments also allow input toselect a map feature or object (e.g., a building) and highlight theobject, producing a blur effect that maintains the virtual camera'sperception of three-dimensional space.

In some embodiments, a client device implements a navigation system(e.g., turn-by-turn navigation). A navigation system provides directionsor route information, which may be displayed to a user. Some embodimentsof a client device request directions or a route calculation from a mapservice. A client device may receive map image data and route data froma map service. In some embodiments, a client device implements aturn-by-turn navigation system, which provides real-time route anddirection information based upon location information and routeinformation received from a map service and/or other location system,such as Global Positioning Satellite (GPS). A client device may displaymap image data that reflects the current location of the client deviceand update the map image data in real-time. A navigation system mayprovide auditory or visual directions to follow a certain route.

A virtual camera is implemented to manipulate navigation map dataaccording to some embodiments. Some embodiments of client devices allowthe device to adjust the virtual camera display orientation to biastoward the route destination. Some embodiments also allow virtual camerato navigation turns simulating the inertial motion of the virtualcamera.

Client devices implement various techniques to utilize map service datafrom map service. Some embodiments implement some techniques to optimizerendering of two-dimensional and three-dimensional map image data. Insome embodiments, a client device locally stores rendering information.For instance, a client stores a style sheet which provides renderingdirections for image data containing style identifiers. In anotherexample, common image textures may be stored to decrease the amount ofmap image data transferred from a map service. Client devices indifferent embodiments implement various modeling techniques to rendertwo-dimensional and three-dimensional map image data, examples of whichinclude, but are not limited to: generating three-dimensional buildingsout of two-dimensional building footprint data; modeling two-dimensionaland three-dimensional map objects to determine the client devicecommunication environment; generating models to determine whether maplabels are seen from a certain virtual camera position; and generatingmodels to smooth transitions between map image data. Some embodiments ofclient devices also order or prioritize map service data in certaintechniques. For instance, a client device detects the motion or velocityof a virtual camera, which if exceeding certain threshold values,lower-detail image data is loaded and rendered of certain areas. Otherexamples include: rendering vector-based curves as a series of points,preloading map image data for areas of poor communication with a mapservice, adapting textures based on display zoom level, or rendering mapimage data according to complexity.

In some embodiments, client devices communicate utilizing various dataformats separate from a map tile. For instance, some client devicesimplement Assisted Global Positioning Satellites (A-GPS) and communicatewith location services that utilize data formats conforming to locationservice protocols, such as, but not limited to, Radio Resource Locationservices Protocol (RRLP), TIA 801 for Code Division Multiple Access(CDMA), Radio Resource Control (RRC) position protocol, or LTEPositioning Protocol (LPP). Client devices may also receive GPS signalsdirectly. Embodiments may also send data, with or without solicitationfrom a map service, identifying the client device's capabilities orattributes (e.g., hardware specifications or operating system version)or communication capabilities (e.g., device communication bandwidth asdetermined by wireless signal strength or wire or wireless networktype).

FIG. 12 illustrates one possible embodiment of an operating environment1200 for a map service 1230 and client devices 1202 a-1202 c. In someembodiments, devices 1202 a, 1202 b, and 1202 c communicate over one ormore wire or wireless networks 1210. For example, wireless network 1210,such as a cellular network, can communicate with a wide area network(WAN) 1220, such as the Internet, by use of gateway 1214. A gateway 1214in some embodiments provides a packet oriented mobile data service, suchas General Packet Radio Service (GPRS), or other mobile data serviceallowing wireless networks to transmit data to other networks, such aswide area network 1220. Likewise, access device 1212 (e.g., IEEE 802.11g wireless access device) provides communication access to WAN 1220.Devices 1202 a and 1202 b can be any portable electronic or computingdevice capable of communicating with a map service. Device 1202 c can beany non-portable electronic or computing device capable of communicatingwith a map service.

In some embodiments, both voice and data communications are establishedover wireless network 1210 and access device 1212. For instance, device1202 a can place and receive phone calls (e.g., using voice overInternet Protocol (VoIP) protocols), send and receive e-mail messages(e.g., using Simple Mail Transfer Protocol (SMTP) or Post OfficeProtocol 3 (POP3)), and retrieve electronic documents and/or streams,such as web pages, photographs, and videos, over wireless network 1210,gateway 1214, and WAN 1220 (e.g., using Transmission ControlProtocol/Internet Protocol (TCP/IP) or User Datagram Protocol (UDP)).Likewise, in some implementations, devices 1202 b and 1202 c can placeand receive phone calls, send and receive e-mail messages, and retrieveelectronic documents over access device 1212 and WAN 1220. In variousembodiments, any of the illustrated client device may communicate withmap service 1230 and/or other service(s) 1250 using a persistentconnection established in accordance with one or more securityprotocols, such as the Secure Sockets Layer (SSL) protocol or theTransport Layer Security (TLS) protocol.

Devices 1202 a and 1202 b can also establish communications by othermeans. For example, wireless device 1202 a can communicate with otherwireless devices (e.g., other devices 1202 b, cell phones, etc.) overthe wireless network 1210. Likewise devices 1202 a and 1202 b canestablish peer-to-peer communications 1240 (e.g., a personal areanetwork) by use of one or more communication subsystems, such asBluetooth® communication from Bluetooth Special Interest Group, Inc. ofKirkland, Wash. Device 1202 c can also establish peer to peercommunications with devices 1202 a or 1202 b (not shown). Othercommunication protocols and topologies can also be implemented. Devices1202 a and 1202 b may also receive Global Positioning Satellite (GPS)signals from GPS satellites 1260.

Devices 1202 a, 1202 b, and 1202 c can communicate with map service 1230over the one or more wire and/or wireless networks, 1210 or 1212. Forinstance, map service 1230 can provide a map service data to renderingdevices 1202 a, 1202 b, and 1202 c. Map service 1230 may alsocommunicate with other services 1250 to obtain data to implement mapservices. Map service 1230 and other services 1250 may also receive GPSsignals from GPS satellites 1260.

In various embodiments, map service 1230 and/or other service(s) 1250are configured to process search requests from any of client devices.Search requests may include but are not limited to queries for business,address, residential locations, points of interest, or some combinationthereof. Map service 1230 and/or other service(s) 1250 may be configuredto return results related to a variety of parameters including but notlimited to a location entered into an address bar or other text entryfield (including abbreviations and/or other shorthand notation), acurrent map view (e.g., user may be viewing one location on themultifunction device while residing in another location), currentlocation of the user (e.g., in cases where the current map view did notinclude search results), and the current route (if any). In variousembodiments, these parameters may affect the composition of the searchresults (and/or the ordering of the search results) based on differentpriority weightings. In various embodiments, the search results that arereturned may be a subset of results selected based on specific criteriainclude but not limited to a quantity of times the search result (e.g.,a particular point of interest) has been requested, a measure of qualityassociated with the search result (e.g., highest user or editorialreview rating), and/or the volume of reviews for the search results(e.g., the number of times the search result has been review or rated).

In various embodiments, map service 1230 and/or other service(s) 1250are configured to provide auto-complete search results that aredisplayed on the client device, such as within the mapping application.For instance, auto-complete search results may populate a portion of thescreen as the user enters one or more search keywords on themultifunction device. In some cases, this feature may save the user timeas the desired search result may be displayed before the user enters thefull search query. In various embodiments, the auto complete searchresults may be search results found by the client on the client device(e.g., bookmarks or contacts), search results found elsewhere (e.g.,from the Internet) by map service 1230 and/or other service(s) 1250,and/or some combination thereof. As is the case with commands, any ofthe search queries may be entered by the user via voice or throughtyping. The multifunction device may be configured to display searchresults graphically within any of the map display described herein. Forinstance, a pin or other graphical indicator may specify locations ofsearch results as points of interest. In various embodiments, responsiveto a user selection of one of these points of interest (e.g., a touchselection, such as a tap), the multifunction device is configured todisplay additional information about the selected point of interestincluding but not limited to ratings, reviews or review snippets, hoursof operation, store status (e.g., open for business, permanently closed,etc.), and/or images of a storefront for the point of interest. Invarious embodiments, any of this information may be displayed on agraphical information card that is displayed in response to the user'sselection of the point of interest.

In various embodiments, map service 1230 and/or other service(s) 1250provide one or more feedback mechanisms to receive feedback from clientdevices 1202 a-1202 c. For instance, client devices may provide feedbackon search results to map service 1230 and/or other service(s) 1250(e.g., feedback specifying ratings, reviews, temporary or permanentbusiness closures, errors etc.); this feedback may be used to updateinformation about points of interest in order to provide more accurateor more up-to-date search results in the future. In some embodiments,map service 1230 and/or other service(s) 1250 may provide testinginformation to the client device (e.g., an A/B test) to determine whichsearch results are best. For instance, at random intervals, the clientdevice may receive and present two search results to a user and allowthe user to indicate the best result. The client device may report thetest results to map service 1230 and/or other service(s) 1250 to improvefuture search results based on the chosen testing technique, such as anA/B test technique in which a baseline control sample is compared to avariety of single-variable test samples in order to improve results.

While the invention has been described with reference to numerousspecific details, one of ordinary skill in the art will recognize thatthe invention can be embodied in other specific forms without departingfrom the spirit of the invention. For instance, many of the figuresillustrate various touch gestures (e.g., taps, double taps, swipegestures, press and hold gestures, etc.). However, many of theillustrated operations could be performed via different touch gestures(e.g., a swipe instead of a tap, etc.) or by non-touch input (e.g.,using a cursor controller, a keyboard, a touchpad/trackpad, a near-touchsensitive screen, etc.). Thus, one of ordinary skill in the art wouldunderstand that the invention is not to be limited by the foregoingillustrative details, but rather is to be defined by the appendedclaims.

The invention claimed is:
 1. A mobile device comprising: a locationidentification engine for identifying a location of the device; aninterface for receiving addresses from a set of other devices associatedwith the device through a network; a data storage for storing pastlocations of the device and addresses received from the set of otherdevices; a prediction engine for predicting future destinations of thedevice based on the stored past locations of the device and addressesreceived from the set of other devices; and a set of processing unitsfor executing the location identification engine and the predictionengine.
 2. The mobile device of claim 1, wherein the addresses receivedfrom the set of other devices comprises a set of addresses harvested bya device of the set of other devices.
 3. The mobile device of claim 1,wherein the mobile device is associated with a particular user and theset of other devices are also associated with the particular user. 4.The mobile device of claim 1 further comprising a calendar and at leastone electronic messaging program, wherein the data storage is furtherfor storing data regarding locations of calendared events in thecalendar, and addresses parsed from electronic messages received by theelectronic messaging program.
 5. The mobile device of claim 4, whereinthe electronic messaging program is at least one of an email program ora text messaging program.
 6. The mobile device of claim 1, wherein thepast locations comprise destinations of past routes traversed by thedevice.
 7. The mobile device of claim 1, wherein the past locationscomprise locations along past routes traversed by the device.
 8. Amobile device comprising: a location identification engine foridentifying a location of the device; an electronic ticket application;a data storage for storing past locations of the device and dataregarding a location of an event for which the electronic ticketapplication has a ticket; a prediction engine for predicting futuredestinations of the device; and a set of processing units for executingthe location identification engine and the prediction engine.
 9. Themobile device of claim 1, wherein the set of other devices comprises adesktop computer and the addresses received from the desktop computercomprise a set of addresses harvested from applications that execute onthe desktop computer.
 10. The mobile device of claim 1, wherein themobile device and the set of other devices are part of a group ofdevices that synchronize their content through a network-basedsynchronization system, wherein the received addresses are addressesextracted by at least one other device that is part of the group. 11.The mobile device of claim 1 further comprising a map application thatgenerates a map display, receives data regarding a predicted destinationfrom the prediction engine, and displays the predicted destination onthe map display.
 12. The mobile device of claim 1 further comprising amap application for generating a map display, wherein the predictionengine is further for formulating routes to predicted destinations andproviding the routes to the map application, wherein the map applicationis further for displaying the predicted destination on the map display.13. The mobile device of claim 12, wherein the prediction enginecomprises a machine-learning engine for formulating predicted futuredestinations and future routes to the predicted future destinations. 14.A non-transitory machine readable medium storing a program for providinginformation about potential destinations to which a device may travel,the program for execution by at least one processing unit, the programcomprising sets of instructions for: identifying a location of thedevice; storing past locations of the device and event location dataregarding locations of events for which an electronic ticket applicationhas a ticket, the electronic ticket application for maintainingelectronic tickets for events; predicting at least one futuredestination of the device based on the stored past locations and theevent location data; and providing an output based on the predicteddestination.
 15. The non-transitory machine readable medium of claim 14,wherein the program further comprises a set of instructions forformulating a route to the predicted destination.
 16. The non-transitorymachine readable medium of claim 14, wherein the program furthercomprises sets of instruction for: extracting locations of calendaredevents stored by a calendar application on the device and extractingaddresses contained in electronic messages received by the device; andusing the extracted locations and addresses along with the stored pastlocations and event location data to predict the future destination. 17.The non-transitory machine readable medium of claim 14, wherein theprogram further comprises sets of instructions for: receiving addressesfrom a set of other devices associated with the device through anetwork; and using the received addresses along with the stored pastlocations and event location data to predict the future destination. 18.The non-transitory machine readable medium of claim 14, wherein the pastlocations comprise locations along past routes traversed by the device.19. The non-transitory machine readable medium of claim 14, wherein thepast locations comprise past destinations of the device.
 20. Anon-transitory machine readable medium storing a program for providinginformation about potential destinations that a device may travel to,the program for execution by at least one processing unit, the programcomprising sets of instructions for: identifying a location of thedevice; receiving addresses from a set of other devices associated withthe device through a network; storing past locations of the device andthe addresses received from the set of other devices; predicting atleast one future destination of the device based on the stored, pastlocations and the addresses received from the set of other devices; andproviding an output based on the predicted destination.
 21. Thenon-transitory machine readable medium of claim 20, wherein the programfurther comprises sets of instructions for: extracting locations ofcalendared events stored by a calendar application on the device andextracting addresses contained in electronic messages received by thedevice; and using the extracted locations and addresses along with thestored past locations and event location data to predict the futuredestination.
 22. The non-transitory machine readable medium of claim 20,wherein the program further comprises a set of instructions for:generating a map display; receiving data regarding a predicteddestination from the prediction engine; and displaying the predicteddestination on the map display.
 23. The non-transitory machine readablemedium of claim 20, wherein the device and the set of other devices arepart of a group of devices that synchronize content.
 24. The mobiledevice of claim 8 further comprising a map application that generates amap display, receives data regarding a predicted destination from theprediction engine, and displays the predicted destination on the mapdisplay.
 25. The mobile device of claim 8 further comprising a mapapplication for generating a map display, wherein the prediction engineis further for formulating routes to predicted destinations andproviding the routes to the map application, wherein the map applicationis further for displaying the predicted destination on the map display.26. The mobile device of claim 8 further comprising an address harvesterfor extracting locations of calendared events stored by a calendarapplication on the device and extracting addresses contained inelectronic messages received by the device, wherein the predictionengine further uses the extracted locations and addresses along with thestored past locations and event location data to predict the futuredestination.